Emergent Labs Transforms Coding with MongoDB

THE CHALLENGE

Building a platform where AI agents code seamlessly

When twin brothers Mukund and Madhav Jha founded Emergent Labs in 2025, they envisioned a platform where anyone could build production-ready software applications using natural language prompts. Through “vibe coding,” users describe what to build while autonomous AI agents write the code, test it, and refine it based on their feedback.

“The software industry has been like a movie production house, where you need a big budget and large teams to produce software,” said Mukund Jha, Co-founder and CEO of Emergent Labs. “With a platform like ours, we’re truly trying to democratize software development, where anybody with an idea and a computer can build what they want.”

To bring this vision to life, Emergent Labs needed a database that wouldn’t create bottlenecks as schemas evolved and applications grew in complexity. The Emergent Labs team initially tried PostgreSQL, but it found that the agents frequently got stuck in schema migration loops when trying to add or delete columns as users added new functionality to their applications. 

This was a critical problem because Emergent Labs’ users—entrepreneurs, small business owners, and nontechnical team members—rarely arrive with fully formed specifications. They develop their ideas as they build, test, and refine in real time.

OUR SOLUTION

Using MongoDB Atlas to power AI-driven app development for Emergent Labs

Before its official launch, Emergent Labs used its platform to build 100 sample applications and test which database worked best with its AI agents. The company discovered that MongoDB Atlas and its flexible structure aligned perfectly with how its agents think and work.

The natural fit was due to the document-based architecture of MongoDB, which naturally works with JSON data. In MongoDB, AI agents can work without any migrations to schema, which leads to faster iteration and prototyping with fewer errors. Because these agents often generate and consume information in JSON, interacting with MongoDB Atlas is intuitive and efficient. This reduces the translation code required for moving data into relational tables—the very process that caused errors and slowdowns during the company’s initial testing.

Emergent Labs implemented MongoDB Atlas as the default database for every full-stack application deployed on its platform. When users prompt the AI agents to build an application, the initial build typically takes just 5 to 20 minutes to produce a first working version. As users move from development to deployment, MongoDB Atlas scales reliably and transparently, enabling a smooth transition to production without the friction Emergent Labs experienced with other databases during testing.

Users work within an isolated MongoDB environment as they test and refine their applications. When they’re satisfied with their application and ready to deploy, the AI agents automatically provision a dedicated MongoDB Atlas database. Each deployed application gets deployed on a MongoDB cluster with an isolated database, helping to maintain complete data privacy and separation.

This flexibility proved especially critical for nontechnical users who continually adjust data structures and add new functionality. As users refine their ideas, MongoDB Atlas adapts without requiring complex migrations or causing major agent errors. The AI agents can create collections on the fly, modify schemas dynamically, and handle changing requirements, all with significantly fewer errors than Emergent Labs observed with PostgreSQL during testing. This not only accelerated development for users but also dramatically simplified backend operations for the Emergent Labs team, reducing the management overhead typically associated with multitenant database architectures.

“We found very early on that MongoDB Atlas was the best choice for our platform, especially for nondevelopers,” said Jha. “These users care the most about their end-use case, so we can recommend the best defaults for an agentic app-building experience.”

OUTCOME

Scaling democratized software development worldwide

In the four months since Emergent Labs’ public launch, the platform has supported the creation of nearly 2 million applications, with 50,000 successfully deployed and serving users across 180 countries. The deployment rate has doubled from 10% to 20% in three months. Approximately two-thirds of power users, who build more complex applications through multiple extended development sessions, are now taking their applications live.

“MongoDB Atlas is the easiest database for an AI agent to work with,” said Jha. “The scaling properties are great, especially now. Almost 2 million apps have been built on the platform, and multi thousands of them deployed. We don’t have to worry about scaling or management, and that’s been really helpful.”

While other vibe-coding platforms typically support applications of 10,000 to 15,000 lines of code before hitting limitations, Emergent Labs’ median application contains 35,000 lines of code. Some complex applications have reached 300,000 lines of code, all supported by MongoDB Atlas.

Emergent Labs’ mission to democratize software development is becoming a reality. Small business owners are digitizing operations with custom tools. Nontechnical teams in larger organizations are building dashboards and demos without waiting for scarce engineering resources. Teachers are creating educational games for their students. Entrepreneurs are launching businesses built entirely on the platform.

As Emergent Labs continues to refine its AI agents and expand its capabilities, MongoDB Atlas will remain the foundation. This technology enables anyone with an idea to become a software creator, transforming development into an accessible medium for users everywhere.

Ready to accelerate your AI development? See why MongoDB is the ideal platform to power innovative AI applications.